HJ
H.Y. Jia
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Indoor localization is used in applications such as emergency response, healthcare, and navigation in large indoor spaces. Global Navigation Satellite System (GNSS) is widely used outdoors but is unreliable indoors due to signal obstructions. In addition, many indoor localization methods depend on dedicated infrastructure. As an infrastructure-free alternative, handheld smartphone Pedestrian Dead Reckoning (PDR) uses inertial sensors but accumulates drift over time. This thesis reduces that drift by integrating handheld smartphone PDR with one-dimensional magnetic-field Simultaneous Localization and Mapping (SLAM), which jointly estimates the pedestrian trajectory while building a map from sensor measurements. It uses a one-dimensional magnetic map along the walked path, which is more lightweight than constructing full two-dimensional or three-dimensional magnetic maps. The drift is reduced by detecting magnetic-field loop closures, i.e., revisits to previously traversed locations. First, a handheld smartphone PDR trajectory is estimated from detected steps, estimated step lengths, and headings obtained from inertial sensing and orientation filtering. The PDR provides the dynamical model for the Kalman Filter (KF) position estimate update. To correct drift, three-axis magnetometer measurements are used to detect loop closures by matching magnetic sequences along the walked path. Magnetic similarity is combined with the KF positional uncertainty to score candidate loop closures. Candidates are validated using temporal constraints, a magnetic-field excitation condition, and a marginal-likelihood test based on the KF innovation. Accepted loop closures are incorporated as landmark constraints by augmenting the KF state, yielding a corrected trajectory. The method is evaluated on three real-world handheld smartphone datasets collected inside a university building over approximately 500 m. In addition to standard walking, the evaluation includes PDR failure modes such as heading drift and step-length underestimation to test robustness, and shows that one-dimensional magnetic-field SLAM can reduce their impact. Four magnetic sequence-matching loop-closure detection methods are compared; the results indicate that using magnetic-field sequences downsampled to footsteps provides the best loop-closure detection. For the nominal walk, magnetic-field SLAM reduces the Absolute Trajectory Error from 5.5 m to 1.8 m and the final error from 11.0 m to 1.0 m.
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Indoor localization is used in applications such as emergency response, healthcare, and navigation in large indoor spaces. Global Navigation Satellite System (GNSS) is widely used outdoors but is unreliable indoors due to signal obstructions. In addition, many indoor localization methods depend on dedicated infrastructure. As an infrastructure-free alternative, handheld smartphone Pedestrian Dead Reckoning (PDR) uses inertial sensors but accumulates drift over time. This thesis reduces that drift by integrating handheld smartphone PDR with one-dimensional magnetic-field Simultaneous Localization and Mapping (SLAM), which jointly estimates the pedestrian trajectory while building a map from sensor measurements. It uses a one-dimensional magnetic map along the walked path, which is more lightweight than constructing full two-dimensional or three-dimensional magnetic maps. The drift is reduced by detecting magnetic-field loop closures, i.e., revisits to previously traversed locations. First, a handheld smartphone PDR trajectory is estimated from detected steps, estimated step lengths, and headings obtained from inertial sensing and orientation filtering. The PDR provides the dynamical model for the Kalman Filter (KF) position estimate update. To correct drift, three-axis magnetometer measurements are used to detect loop closures by matching magnetic sequences along the walked path. Magnetic similarity is combined with the KF positional uncertainty to score candidate loop closures. Candidates are validated using temporal constraints, a magnetic-field excitation condition, and a marginal-likelihood test based on the KF innovation. Accepted loop closures are incorporated as landmark constraints by augmenting the KF state, yielding a corrected trajectory. The method is evaluated on three real-world handheld smartphone datasets collected inside a university building over approximately 500 m. In addition to standard walking, the evaluation includes PDR failure modes such as heading drift and step-length underestimation to test robustness, and shows that one-dimensional magnetic-field SLAM can reduce their impact. Four magnetic sequence-matching loop-closure detection methods are compared; the results indicate that using magnetic-field sequences downsampled to footsteps provides the best loop-closure detection. For the nominal walk, magnetic-field SLAM reduces the Absolute Trajectory Error from 5.5 m to 1.8 m and the final error from 11.0 m to 1.0 m.